Abstract
The sliding mode controller stands out for its exceptional stability, even when the system experiences noise or undergoes time-varying parameter changes. However, designing a sliding mode controller necessitates precise knowledge of the object’s exact model, which is often unattainable in practical scenarios. Furthermore, if the sliding control law’s amplitude becomes excessive, it can lead to undesirable chattering phenomena near the sliding surface. This article presents a new method that uses a special kind of computer program (Radial Basis Function Neural Network) to quickly calculate complex relationships in a robot’s control system. This calculation is combined with a technique called Sliding Mode Control, and Fuzzy Logic is used to measure the size of the control action, all while making sure the system stays stable using Lyapunov stability theory. We tested this new method on a robot arm that can move in three different ways at the same time, showing that it can handle complex, multiple-input, multiple-output systems. In addition, applying LPV combined with Kalman helps reduce noise and the system operates more stably. The manipulator’s response under this controller exhibits controlled overshoot (Rad), with a rise time of approximately 5 ± 3% seconds and a settling error of around 1%. These control results are rigorously validated through simulations conducted using MATLAB/Simulink software version 2022b. This research contributes to the advancement of control strategies for robotic manipulators, offering improved stability and adaptability in scenarios where precise system modeling is challenging.